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<p>Municipal solid waste collection in most Indian cities still relies on fixed-schedule collection and static routing, irrespective of bin fill levels. It often results in overflowing bins in high-waste areas, unnecessary trips to partially filled bins elsewhere, increased fuel consumption, increased operational costs, and&#xa0;environmental pollution. In this study, a hypothetical&#xa0;simulation-based case study of an IoT-enabled smart waste collection and Route Optimization System is designed to support data-driven&#xa0;waste collection planning. Simulated ultrasonic bin level readings were transmitted to a cloud dashboard for real-time monitoring and alert generation. When bin fill levels exceeded a predefined threshold,&#xa0;the Capacitated Vehicle Routing Problem for the shortest-path algorithm&#xa0;was applied to compute an optimized route for waste-collection vehicles. The results demonstrate that the proposed system has&#xa0;reduced total route distance by approximately 35–40%, fuel consumption by 30–35%, and operational costs by 30–33% compared to traditional fixed-route&#xa0;collection. The reduction in fuel use also translated to annual CO₂ emission savings of approximately 2–3 tons per vehicle.&#xa0;This case study demonstrates that a simulation-driven IoT architecture can achieve efficient, cost-effective solid waste management with meaningful environmental benefits.</p>

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An Approach for IoT-enabled Smart Waste Collection and Route Optimization System for Solid Waste Management

  • 期刊论文

  • 发表日期：

    2026年7月22日

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  • Prerna Goyal,
  • Kapisha Nigam,
  • Nekram Rawal

摘要

Municipal solid waste collection in most Indian cities still relies on fixed-schedule collection and static routing, irrespective of bin fill levels. It often results in overflowing bins in high-waste areas, unnecessary trips to partially filled bins elsewhere, increased fuel consumption, increased operational costs, and environmental pollution. In this study, a hypothetical simulation-based case study of an IoT-enabled smart waste collection and Route Optimization System is designed to support data-driven waste collection planning. Simulated ultrasonic bin level readings were transmitted to a cloud dashboard for real-time monitoring and alert generation. When bin fill levels exceeded a predefined threshold, the Capacitated Vehicle Routing Problem for the shortest-path algorithm was applied to compute an optimized route for waste-collection vehicles. The results demonstrate that the proposed system has reduced total route distance by approximately 35–40%, fuel consumption by 30–35%, and operational costs by 30–33% compared to traditional fixed-route collection. The reduction in fuel use also translated to annual CO₂ emission savings of approximately 2–3 tons per vehicle. This case study demonstrates that a simulation-driven IoT architecture can achieve efficient, cost-effective solid waste management with meaningful environmental benefits.